OpenAlex Academic Search

Search query: $ARGUMENTS

Role & Positioning

This skill uses OpenAlex as a comprehensive open academic graph source:

Skill	Source	Best for
/arxiv	arXiv API	Latest preprints, cutting-edge unrefereed work
/semantic-scholar	Semantic Scholar API	Published venue papers (IEEE, ACM, Springer) with citation counts
/openalex	OpenAlex API	Open citation graph, institutional affiliations, funding data, comprehensive metadata
/deepxiv	DeepXiv CLI	Layered reading: search, brief, section map, section reads
/exa-search	Exa API	Broad web search: blogs, docs, news, companies, research papers
/gemini-search	Gemini MCP / CLI	AI-powered broad literature discovery

Use OpenAlex when you want:

• Open citation data — fully open citation graph (no API key required for basic use)
• Institutional affiliations — author institutions and collaborations
• Funding information — NSF, NIH, and other funding sources
• Comprehensive metadata — topics, keywords, abstract, open access status
• Cross-database coverage — indexes 250M+ works from multiple sources

Constants

• MAX_RESULTS = 10 — Default number of results. Override with — max: 20.
• DEFAULT_SORT = relevance — Sort by relevance. Override with — sort: citations or — sort: date.
• OPENALEX_FETCHER — canonical name openalex_fetch.py, resolved per shared-references/integration-contract.md §2 (Policy D1 — standalone /openalex has no documented inline fallback, so unresolved helper terminates with an explicit error).

Overrides (append to arguments):

• /openalex "topic" — max: 20 — return up to 20 results
• /openalex "topic" — year: 2023- — papers from 2023 onward
• /openalex "topic" — year: 2020-2023 — papers from 2020 to 2023
• /openalex "topic" — type: article — only journal articles
• /openalex "topic" — type: preprint — only preprints
• /openalex "topic" — open-access — only open access papers
• /openalex "topic" — min-citations: 50 — minimum 50 citations
• /openalex "topic" — sort: citations — sort by citation count (descending)
• /openalex "topic" — sort: date — sort by publication date (newest first)

Setup

Prerequisites

1. Python 3.7+ with requests library:

   pip install requests
   

2. Optional: API keys — Export them in your shell or project environment:

   export OPENALEX_API_KEY=your-key-here
   export OPENALEX_EMAIL=your-email@example.com
   

3. Get API keys (optional but recommended):

   • OpenAlex API key: Free tier $1/day (10,000 list calls, 1,000 search calls) from openalex.org
   • Email for polite pool: Faster response times (no registration needed)

Verify Setup

python3 "$OPENALEX_FETCHER" search "machine learning" --max 3

(Resolve $OPENALEX_FETCHER via the canonical chain first — see Step 2 below.)

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

• query: The research topic (required)
• max: Override MAX_RESULTS
• year: Publication year filter (e.g., 2023-, 2020-2023)
• type: Work type filter (article, preprint, book, book-chapter, dataset, dissertation)
• open-access: Only include open access papers
• min-citations: Minimum citation count threshold
• sort: Sort order (relevance, citations, date)

Step 2: Locate Script

Resolve $OPENALEX_FETCHER via the canonical strict-safe chain (see shared-references/integration-contract.md §2). Policy D1: there is no native inline fallback for OpenAlex (retrieval requires the requests SDK + optional API key — the fetcher script encapsulates pagination, throttling, and per-source parameters), so unresolved helper terminates with explicit remediation.

cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
OPENALEX_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/openalex_fetch.py" ] && OPENALEX_FETCHER="$ARIS_REPO/tools/openalex_fetch.py"
[ -z "$OPENALEX_FETCHER" ] && [ -f tools/openalex_fetch.py ] && OPENALEX_FETCHER="tools/openalex_fetch.py"
[ -z "$OPENALEX_FETCHER" ] && [ -f ~/.codex/skills/openalex/openalex_fetch.py ] && OPENALEX_FETCHER="$HOME/.codex/skills/openalex/openalex_fetch.py"
[ -f "$OPENALEX_FETCHER" ] || {
  echo "ERROR: openalex_fetch.py not resolved at \$ARIS_REPO/tools/, tools/, or ~/.codex/skills/openalex/." >&2
  echo "       Fix: rerun install_aris_codex.sh, export ARIS_REPO, or copy the helper to ~/.codex/skills/openalex/." >&2
  echo "       Also ensure 'requests' is installed: pip install requests" >&2
  exit 1
}

Step 3: Execute Search

Basic search:

python3 "$OPENALEX_FETCHER" search "QUERY" --max 10

With filters:

python3 "$OPENALEX_FETCHER" search "QUERY" --max 10 \
  --year 2023- \
  --type article \
  --open-access \
  --min-citations 20 \
  --sort citations

Get specific work by DOI:

python3 "$OPENALEX_FETCHER" work "10.1109/TWC.2024.1234567"

Get specific work by OpenAlex ID:

python3 "$OPENALEX_FETCHER" work "W2741809807"

Step 4: Parse Results

The script returns structured JSON with:

• title: Paper title
• authors: List of author names
• publication_year: Year published
• venue: Journal/conference name
• venue_type: Type of venue (journal, repository, conference, etc.)
• cited_by_count: Number of citations
• is_oa: Boolean for open access status
• oa_status: Open access type (gold, green, bronze, hybrid, closed)
• oa_url: Direct PDF link if available
• doi: DOI identifier
• openalex_id: OpenAlex work ID
• abstract: Full abstract text
• topics: Top 3 research topics
• keywords: Top 5 keywords
• type: Work type (article, preprint, etc.)

Step 5: Present Results

Format results as a structured table:

| # | Title | Venue | Year | Citations | OA | Summary |
|---|-------|-------|------|-----------|----|---------|
| 1 | ... | IEEE TWC | 2024 | 156 | ✓ | ... |
| 2 | ... | NeurIPS | 2023 | 89 | ✓ | ... |

For each paper, also show:

• DOI: Canonical identifier
• OpenAlex ID: For cross-reference
• Open Access: Status (gold/green/bronze/hybrid/closed) and PDF link
• Topics: Top research topics
• Abstract: First 200 characters or full text

Step 6: Offer Follow-up

After presenting results, suggest:

/semantic-scholar "DOI:..."     — get S2 citation context and related papers
/arxiv "arXiv:XXXX.XXXXX"      — fetch arXiv preprint if available
/research-lit "topic" — sources: openalex, semantic-scholar  — combined multi-source review
/novelty-check "idea"          — verify novelty against literature

Key Rules

• OpenAlex is fully open — no API key required for basic use, but recommended for higher rate limits
• Comprehensive metadata — OpenAlex provides richer metadata than most sources (institutions, funding, topics)
• Citation data is open — unlike Semantic Scholar, all citation data is freely accessible
• Rate limits: Without API key, very limited (~$0.01/day). With free API key: 10,000 list calls/day, 1,000 search calls/day.
• Polite pool: Set OPENALEX_EMAIL environment variable for faster response times
• Cross-reference with other sources: OpenAlex indexes papers from arXiv, PubMed, Crossref, etc. — use DOI/arXiv ID to cross-reference
• If OpenAlex API is unreachable or rate-limited, suggest using /semantic-scholar, /arxiv, or /research-lit "topic" — sources: web as alternatives.

OpenAlex vs Other Sources

Feature	OpenAlex	Semantic Scholar	arXiv
Coverage	250M+ works	200M+ papers	2.4M+ preprints
Citation data	Fully open	Partially open	None
Institutions	✓ Full affiliations	✓ Limited	✗
Funding	✓ NSF, NIH, etc.	✗	✗
Open access	✓ Full OA status	✓ PDF links	✓ All papers
API key	Optional (free)	Optional (free)	Not required
Rate limits	1,000 searches/day (free key)	Unknown	1 req/3s
Abstract	✓ Full text	✓ TLDR	✓ Full text
Best for	Comprehensive metadata, institutions, funding	Citation counts, venue info	Latest preprints

When to use OpenAlex over S2:

• Need institutional affiliation data
• Need funding information
• Want fully open citation graph
• Need comprehensive topic/keyword metadata
• Working with non-CS fields (OpenAlex covers all disciplines)

When to use S2 over OpenAlex:

• Need real-time citation counts (S2 updates faster)
• Need "highly influential citations" metric
• Need paper recommendations
• CS/AI-focused research (S2 has better CS coverage)